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We provide quasi-experimental estimates of the impact of social media on mental health by leveraging a unique natural experiment: the staggered introduction of Facebook across US colleges. Our analysis couples data on student mental health around the years of Facebook’s expansion with a generalized difference-in-differences empirical strategy. We find that the rollout of Facebook at a college had a negative impact on student mental health. It also increased the likelihood with which students reported experiencing impairments to academic performance due to poor mental health. Additional evidence on mechanisms suggests the results are due to Facebook fostering unfavorable social comparisons.

Information provision experiments allow researchers to test economic theories and answer policy-relevant questions by varying the information set available to respondents. We survey the emerging literature using information provision experiments in economics and discuss applications in macroeconomics, finance, political economy, public economics, labor economics, and health economics. We also discuss design considerations and provide best-practice recommendations on how to (i) measure beliefs; (ii) design the information intervention; (iii) measure belief updating; (iv) deal with potential confounds, such as experimenter demand effects; and (v) recruit respondents using online panels. We finally discuss typical effect sizes and provide sample size recommendations.

We review the recent literature on rational inattention, identify the main theoretical mechanisms, and explain how it helps us understand a variety of phenomena across fields of economics. The theory of rational inattention assumes that agents cannot process all available information, but they can choose which exact pieces of information to attend to. Several important results in economics have been built around imperfect information. Nowadays, many more forms of information than ever before are available due to new technologies, and yet we are able to digest little of it. Which form of imperfect information we possess and act upon is thus largely determined by which information we choose to pay attention to. These choices are driven by current economic conditions and imply behavior that features numerous empirically supported departures from standard models. Combining these insights about human limitations with the optimizing approach of neoclassical economics yields a new, generally applicable model.

We propose a technique for assessing robustness to demand effects of findings from experiments and surveys. The core idea is that by deliberately inducing demand in a structured way we can bound its influence. We present a model in which participants respond to their beliefs about the researcher’s objectives. Bounds are obtained by manipulating those beliefs with “demand treatments.” We apply the method to 11 classic tasks, and estimate bounds averaging 0.13 standard deviations, suggesting that typical demand effects are probably modest. We also show how to compute demand-robust treatment effects and how to structurally estimate the model.

Nudges are popular types of interventions. Recent years have seen the rise of ‘norm-nudges’—nudges whose mechanism of action relies on social norms, eliciting or changing social expectations. Norm-nudges can be powerful interventions, but they can easily fail to be effective and can even backfire unless they are designed with care. We highlight important considerations when designing norm-nudges and discuss a general model of social behavior based on social expectations and conditional preferences. We present the results of several experiments wherein norm-nudging can backfire, and ways to avoid those negative outcomes.

In many areas, practitioners seek to use observational data to learn a treatment assignment policy that satisfies application-specific constraints, such as budget, fairness, simplicity, or other functional form constraints. For example, policies may be restricted to take the form of decision trees based on a limited set of easily observable individual characteristics. We propose a new approach to this problem motivated by the theory of semiparametrically efficient estimation. Our method can be used to optimize either binary treatments or infinitesimal nudges to continuous treatments, and can leverage observational data where causal effects are identified using a variety of strategies, including selection on observables and instrumental variables. Given a doubly robust estimator of the causal effect of assigning everyone to treatment, we develop an algorithm for choosing whom to treat, and establish strong guarantees for the asymptotic utilitarian regret of the resulting policy.

We leverage a large-scale incentivized survey eliciting behaviors from (almost) an entire undergraduate university student population, a representative sample of the US population, and Amazon Mechanical Turk (MTurk) to address concerns about the external validity of experiments with student participants. Behavior in the student population offers bounds on behaviors in other populations, and correlations between behaviors are similar across samples. Furthermore, non-student samples exhibit higher levels of noise. Adding historical lab participation data, we find a small set of attributes over which lab participants differ from non-lab participants. An additional set of lab experiments shows no evidence of observer effects.

The realm of entrepreneurship has seen a rise in research on effectuation from the perspective of cognition, which has sparked significant discussion among academics due to a lack of well-defined theoretical foundations. However, despite this interest in cognitive theories, the grounded cognition theory has not been adequately explored to explain the behavior of entrepreneurs. Accordingly, we propose an integrative theoretical framework for the effectuation and causation models in light of an offloading process. This process helps to explain the relationship between the entrepreneur’s cognitive antecedents and their behavioral outcomes. Consequently, our study provides theoretical underpinnings for effectuation and a better understanding of how effectuation and causation models are alternatingly engaged during the entrepreneur’s decision-making process.Plain English SummaryThe entrepreneur’s behavior explained by the grounded cognition theory: how and why effectuation and causation are complementary models of decision-making? This research draws on grounded cognition theory and aims to deepen our understanding of the entrepreneurial decision-making process through the notion of “offloading.” It also discusses its behavioral consequences according to effectuation and causation models. This research theoretically explains the basis for effectuation and suggests an integrative framework for the entrepreneurial decision-making process, which is critically needed in the current body of research. By understanding the complementary nature of the two models, entrepreneurs can gain a better understanding of their own decision-making process and improve their overall practices. This research therefore strengthens entrepreneurs’ awareness of the point at which they switch from one process to another, thereby legitimizing their decision-making process, by improving representation of the entrepreneurial decision-making process. This research therefore helps us understand the business practices of entrepreneurs.

Nudge interventions have quickly expanded from academic studies to larger implementation in so-called Nudge Units in governments. This provides an opportunity to compare interventions in research studies, versus at scale. We assemble a unique data set of 126 RCTs covering 23 million individuals, including all trials run by two of the largest Nudge Units in the United States. We compare these trials to a sample of nudge trials in academic journals from two recent meta-analyses. In the Academic Journals papers, the average impact of a nudge is very large—an 8.7 percentage point take-up effect, which is a 33.4% increase over the average control. In the Nudge Units sample, the average impact is still sizable and highly statistically significant, but smaller at 1.4 percentage points, an 8.0% increase. We document three dimensions which can account for the difference between these two estimates: (i) statistical power of the trials; (ii) characteristics of the interventions, such as topic area and behavioral channel; and (iii) selective publication. A meta-analysis model incorporating these dimensions indicates that selective publication in the Academic Journals sample, exacerbated by low statistical power, explains about 70 percent of the difference in effect sizes between the two samples. Different nudge characteristics account for most of the residual difference.

A key question in the literature on motivated reasoning and self-deception is how motivated beliefs are sustained in the presence of feedback. In this paper, we explore dynamic motivated belief patterns after feedback. We establish that positive feedback has a persistent effect on beliefs. Negative feedback, instead, influences beliefs in the short run, but this effect fades over time. We investigate the mechanisms of this dynamic pattern, and provide evidence for an asymmetry in the recall of feedback. Finally, we establish that, in line with theoretical accounts, incentives for belief accuracy mitigate the role of motivated reasoning.